cutlass/tools/library/scripts/pycutlass/test/gemm/gemm_grouped_sm80.py

import pycutlass
from pycutlass import *
from pycutlass.test import *
import unittest

from pycutlass.test.gemm_grouped_testbed import TestbedGrouped
from pycutlass.utils.device import device_cc


@unittest.skipIf(device_cc() < 80, "Device compute capability is insufficient for SM80 tests.")
class GemmGroupedSm80(unittest.TestCase):
    def test_SM80_Device_GemmGrouped_f16n_f16t_f32n_tensor_op_f32_128x128x32_64x64x32(self):
        math_inst = MathInstruction(
            instruction_shape=[16, 8, 16], element_a=cutlass.float16,
            element_b=cutlass.float16, element_accumulator=cutlass.float32,
            opcode_class=cutlass.OpClass.TensorOp,
            math_operation=MathOperation.multiply_add
        )

        tile_description = TileDescription(
            threadblock_shape=[128, 128, 32],
            stages=3, warp_count=[2, 2, 1],
            math_instruction=math_inst
        )

        A = TensorDescription(
            element=cutlass.float16, layout=cutlass.ColumnMajor,
            alignment=8
        )

        B = TensorDescription(
            element=cutlass.float16, layout=cutlass.ColumnMajor,
            alignment=8
        )

        C = TensorDescription(
            element=cutlass.float32, layout=cutlass.ColumnMajor,
            alignment=4
        )

        element_epilogue = cutlass.float32
        epilogue_functor = LinearCombination(
            C.element, C.alignment, 
            math_inst.element_accumulator, element_epilogue)
        swizzling_functor = cutlass.BatchedIdentitySwizzle

        for precompute_mode in [SchedulerMode.Device, SchedulerMode.Host]:
            operation = GemmOperationGrouped(
                80,
                tile_description, A, B, C,
                epilogue_functor, swizzling_functor,
                precompute_mode=precompute_mode
            )

            testbed = TestbedGrouped(operation=operation)

            self.assertTrue(testbed.run(24))
    
    def test_SM80_Device_GemmGrouped_f64t_f64t_f64n_tensor_op_f64_64x64x16_32x32x16(self):
        math_inst = MathInstruction(
            instruction_shape=[8, 8, 4], element_a=cutlass.float64,
            element_b=cutlass.float64, element_accumulator=cutlass.float64,
            opcode_class=cutlass.OpClass.TensorOp,
            math_operation=MathOperation.multiply_add
        )

        tile_description = TileDescription(
            threadblock_shape=[64, 64, 16],
            stages=4, warp_count=[2, 2, 1],
            math_instruction=math_inst
        )

        A = TensorDescription(
            element=cutlass.float64, layout=cutlass.RowMajor,
            alignment=1
        )

        B = TensorDescription(
            element=cutlass.float64, layout=cutlass.RowMajor,
            alignment=1
        )

        C = TensorDescription(
            element=cutlass.float64, layout=cutlass.ColumnMajor,
            alignment=1
        )

        element_epilogue = cutlass.float64
        epilogue_functor = LinearCombination(
            C.element, C.alignment, 
            math_inst.element_accumulator, element_epilogue)
        swizzling_functor = cutlass.BatchedIdentitySwizzle

        for precompute_mode in [SchedulerMode.Device, SchedulerMode.Host]:
            operation = GemmOperationGrouped(
                80,
                tile_description, A, B, C,
                epilogue_functor, swizzling_functor,
                precompute_mode=precompute_mode
            )

            testbed = TestbedGrouped(operation=operation)

            self.assertTrue(testbed.run(24))
    
    def test_SM80_Device_GemmGrouped_f32t_f32t_f32t_simt_f32_128x64x8_64x32x1(self):
        math_inst = MathInstruction(
            instruction_shape=[1, 1, 1], element_a=cutlass.float32,
            element_b=cutlass.float32, element_accumulator=cutlass.float32,
            opcode_class=cutlass.OpClass.Simt,
            math_operation=MathOperation.multiply_add
        )

        tile_description = TileDescription(
            threadblock_shape=[128, 64, 8],
            stages=4, warp_count=[2, 2, 1],
            math_instruction=math_inst
        )

        A = TensorDescription(
            element=cutlass.float32, layout=cutlass.RowMajor,
            alignment=1
        )

        B = TensorDescription(
            element=cutlass.float32, layout=cutlass.RowMajor,
            alignment=1
        )

        C = TensorDescription(
            element=cutlass.float32, layout=cutlass.RowMajor,
            alignment=1
        )

        element_epilogue = cutlass.float32
        epilogue_functor = LinearCombination(
            C.element, C.alignment, 
            math_inst.element_accumulator, element_epilogue)
        swizzling_functor = cutlass.BatchedIdentitySwizzle

        for precompute_mode in [SchedulerMode.Device, SchedulerMode.Host]:
            operation = GemmOperationGrouped(
                80,
                tile_description, A, B, C,
                epilogue_functor, swizzling_functor,
                precompute_mode=precompute_mode
            )

            testbed = TestbedGrouped(operation=operation)

            self.assertTrue(testbed.run(27))
    
    def test_SM80_Device_GemmGrouped_f16n_f16t_f32n_tensor_op_f32_128x128x32_64x64x32_cache(self):
        math_inst = MathInstruction(
            instruction_shape=[16, 8, 16], element_a=cutlass.float16,
            element_b=cutlass.float16, element_accumulator=cutlass.float32,
            opcode_class=cutlass.OpClass.TensorOp,
            math_operation=MathOperation.multiply_add
        )

        tile_description = TileDescription(
            threadblock_shape=[128, 128, 32],
            stages=3, warp_count=[2, 2, 1],
            math_instruction=math_inst
        )

        A = TensorDescription(
            element=cutlass.float16, layout=cutlass.ColumnMajor,
            alignment=8
        )

        B = TensorDescription(
            element=cutlass.float16, layout=cutlass.ColumnMajor,
            alignment=8
        )

        C = TensorDescription(
            element=cutlass.float32, layout=cutlass.ColumnMajor,
            alignment=4
        )

        element_epilogue = cutlass.float32
        epilogue_functor = LinearCombination(
            C.element, C.alignment, 
            math_inst.element_accumulator, element_epilogue)
        swizzling_functor = cutlass.BatchedIdentitySwizzle

        for precompute_mode in [SchedulerMode.Device, SchedulerMode.Host]:
            operation = GemmOperationGrouped(
                80,
                tile_description, A, B, C,
                epilogue_functor, swizzling_functor,
                precompute_mode=precompute_mode
            )

            testbed = TestbedGrouped(operation=operation)

            self.assertTrue(testbed.run(5))


if __name__ == '__main__':
    pycutlass.get_memory_pool(2**26, 2**26)
    unittest.main()